{"id":"W4316924672","doi":"10.18609/cgti.2022.209","title":"Innovating in iPSC differentiation & engineering","year":2022,"lang":"en","type":"article","venue":"Cell and Gene Therapy Insights","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chemistry; Cell biology; Engineering; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000640443,0.00009909409,0.00009821755,0.0001535221,0.00006594414,0.00001717981,0.000063566,0.00002983506,0.00004656673],"category_scores_gemma":[0.000001008921,0.00009830696,0.00001547248,0.0002227633,0.000004460242,0.00005202003,0.00002309298,0.0001578176,0.000001585793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003223316,"about_ca_system_score_gemma":0.000003469969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006521651,"about_ca_topic_score_gemma":0.000002849712,"domain_scores_codex":[0.9995488,0.00001643262,0.0001356315,0.0001029488,0.00007526665,0.0001209518],"domain_scores_gemma":[0.9998481,0.0000153133,0.00001521859,0.00009403646,0.000007409559,0.000019915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007441447,0.00003334608,0.0005101311,0.00002613228,0.00001099694,0.000004439097,0.002389926,0.009543923,0.9594152,0.0001742221,0.000179692,0.02770456],"study_design_scores_gemma":[0.001625478,0.0001972679,0.01025566,0.00002396373,0.000006425315,0.00001568474,0.0001002053,0.1222482,0.8047792,0.002240778,0.05763269,0.0008744126],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906726,0.002837452,0.005452305,0.000007041917,0.0001229982,0.0001079258,0.000001592757,0.0002301982,0.0005678731],"genre_scores_gemma":[0.9983059,0.0004880033,0.0009652333,0.00003943524,0.00004117986,0.00005436997,0.00001889801,0.0000224614,0.0000645691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.154636,"threshold_uncertainty_score":0.4008842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01158348569898052,"score_gpt":0.1814730039020362,"score_spread":0.1698895182030556,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}